SPIRIT：基于深度学习不确定性的感知共享自主控制以实现稳健的机器人操作
SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
AI总结：
SPIRIT通过结合深度学习的不确定性估计与触觉遥控，实现安全稳健的机器人操作，提升性能与可靠性。
摘要：
深度学习（DL）在机器人感知方面取得了显著进展，但其有限的鲁棒性和缺乏可解释性阻碍了在安全关键应用中的可靠部署。我们提出了一种称为感知共享自主的概念，其中基于深度学习的感知的不确定性估计用于调节自主程度。具体来说，当机器人的感知自信时，半自主操作被启用以提高性能；当不确定性增加时，控制会过渡到触觉遥控以保持鲁棒性。这样，高性能但不可解释的深度学习方法可以安全地集成到机器人系统中。关键技术之一是基于所谓的神经切线核（NTK）的不确定性感知深度学习点云配准方法。我们通过15名参与者的用户研究和模拟工业场景的实现，评估了感知共享自主在具有挑战性的空中操作任务中的表现，证明了即使在基于深度学习的感知失败时，也能实现可靠的机器人操作。所得到的系统命名为SPIRIT，它在操作性能和系统可靠性方面都得到了提升。SPIRIT被选为一项主要工业创新奖的决赛选手。
英文摘要：
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.
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